code: def strict_mode_func(ctx, callable, inputs):
      unwrapped_inputs = ctx.unwrap_tensors(inputs)
      with ctx.redispatch_to_next():
          functional_callable = ctx.functionalize(callable)
  
          cond_return = strict_mode_op(
              functional_callable, unwrapped_inputs
          )
          return ctx.wrap_tensors(cond_return)
  
file_path: pytorch/torch/_higher_order_ops/strict_mode.py
imports: from contextlib import contextmanager
  import torch
  import torch._subclasses.functional_tensor
  import torch.utils._pytree
  from torch._C import DispatchKey
  from torch._functorch.utils import exposed_in
  from torch._higher_order_ops.utils import autograd_not_implemented
  from torch._ops import HigherOrderOperator
  from torch._subclasses.fake_tensor import FakeTensorMode
  from torch.fx.experimental.proxy_tensor import disable_proxy_modes_tracing, make_fx, ProxyTorchDispatchMode, track_tensor_tree
  from torch.utils._python_dispatch import _get_current_dispatch_mode
label_desc: Use image node_id 7 for calling a global function with example usage: strict_mode_func(ctx, callable, inputs) and returns: ctx